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Social media has become an indispensable resource in disaster response, providing real-time crowdsourced data on public experiences, needs, and conditions during crises. This user-generated content enables government agencies and emergency responders to identify emerging threats, prioritize resource allocation, and optimize relief operations through data-driven insights. We present an AI-powered framework that combines natural language processing with geospatial visualization to analyze disaster-related social media content. Our solution features a text analysis model that achieved an 81.4% F1 score in classifying Twitter/X posts, integrated with an interactive web platform that maps emotional trends and crisis situations across geographic regions. The system’s dynamic visualization capabilities allow authorities to monitor situational developments through an interactive map, facilitating targeted response coordination. The experimental results show the model’s effectiveness in extracting actionable intelligence from Twitter/X posts during natural disasters. 

Natural disasters pose significant threats to human life and property, exacerbated by their sudden onset and increasing frequency. This paper conducts a comprehensive bibliometric review to explore robust methodologies for post-disaster building damage assessment and reconnaissance, focusing on the integration of advanced data collection technologies and computational techniques. The objectives of this study were to assess the current landscape of methodologies, highlight technological advancements, and identify significant trends and gaps in the literature. Using a structured approach for data collection, this review analyzed 370 journal articles from the Scopus database from 2014 to 2024, emphasizing recent developments in remote sensing, including satellite and UAV technologies, and the application of machine learning and deep learning for damage detection and analysis. Our findings reveal substantial advancements in data collection and analysis techniques, underscoring the critical role of machine learning and remote sensing in enhancing disaster damage assessments. The results are significant as they highlight areas requiring further research and development, particularly in data fusion techniques, real-time processing capabilities, model generalization, UAV technology enhancements, and training for the rescue team. These areas are crucial for improving disaster management practices and enhancing community resilience. The application of our research is particularly relevant in developing more effective emergency response strategies and in informing policy-making for disaster-prepared social infrastructure planning. Future research should focus on closing the identified gaps and leveraging cutting-edge technologies to advance the field of disaster management.